Machine Learning-Mediated Analysis of Physical Literacy in Children’s Subjective Well-Being: Evidence from a Multinational Survey

Background/Objectives: Subjective well-being (SWB) in children is a key indicator of healthy development, influenced by physical activity and sports, with physical literacy (PL) as a potential mediator. Traditional linear models overlook non-linear and heterogeneous effects in diverse populations. This study uses causal machine learning (ML) to examine PL’s mediating role between sports participation and SWB in a multinational cohort. Methods: Data from the International Survey of Children’s Well-Being (ISCWeB) (n = 128,184 children aged 6–14, 35 countries) were analyzed. SWB was a composite (six items, α = 0.85); PL was a proxy (three items excluding sports frequency, α = 0.70); sports participation was continuous (0–5). Confounders were age, gender, parental listening, and school satisfaction. CausalForestDML estimated the effects; GroupKFold and bootstrap were used for robustness; SHAP/PDP was used for interpretability. Results: Total ATE = 0.083 (95% CI [0.073, 0.094]); indirect via PL = 0.055 (CI [0.049, 0.061]); direct = 0.028 (CI [0.020, 0.038]); mediation proportion = 0.660. Sensitivity with lean PL (2 items) was as follows: indirect = 0.045 (CI [0.040, 0.050]). For SHAP, school satisfaction was (+0.28), and parents were (+0.20) top. For PDP, there was a non-linear rise at PL 4–6 (+1.2 units) and a plateau ~9.2. The cross-cultural mean ATE = 0.083 ± 0.01 (from within-country meta-analysis); this was stronger in older children (CATE 0.30 for 12–14). For Rho sensitivity at 0.1, it was indirect −0.129; at Rho sensitivity of 0.2, it was −0.314 (robust to low confounding). Conclusions: The findings, grounded in SDT/PYD, support interventions targeting PL through sports to enhance SWB, addressing inactivity. Limitations are its cross-sectional nature and proxy measures; we recommend longitudinal studies.